# FAST-RIR: Fast neural diffuse room impulse response generator

arXiv: https://arxiv.org/abs/2110.04057 · HTML: https://mlsanity.com/paper/2110.04057/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis](https://mlsanity.com/paper/2103.14201/) (2021)
- [TS-RIR: Translated synthetic room impulse responses for speech augmentation](https://mlsanity.com/paper/2103.16804/) (2021)
- [Improving Reverberant Speech Training Using Diffuse Acoustic Simulation](https://mlsanity.com/paper/1907.03988/) (2019)
- [Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization](https://mlsanity.com/paper/2011.00091/) (2020)
- [Sound Synthesis, Propagation, and Rendering: A Survey](https://mlsanity.com/paper/2011.05538/) (2020)
- [Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation in Complex Domain](https://mlsanity.com/paper/2104.12359/) (2021)
- [StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation](https://mlsanity.com/paper/2008.07231/) (2020)
- [Scene-aware Far-field Automatic Speech Recognition](https://mlsanity.com/paper/2104.10757/) (2021)

## Built on by (most cited first, 30 of 35)

- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields](https://mlsanity.com/paper/2309.15977/) (2023)
- [Measuring Acoustics with Collaborative Multiple Agents](https://mlsanity.com/paper/2310.05368/) (2023)
- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark](https://mlsanity.com/paper/2403.18821/) (2024)
- [AdVerb: Visually Guided Audio Dereverberation](https://mlsanity.com/paper/2308.12370/) (2023)
- [AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis](https://mlsanity.com/paper/2406.08920/) (2024)
- [Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes](https://mlsanity.com/paper/2302.02809/) (2023)
- [Towards Improved Room Impulse Response Estimation for Speech Recognition](https://mlsanity.com/paper/2211.04473/) (2022)
- [NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields](https://mlsanity.com/paper/2405.18213/) (2024)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [Novel-View Acoustic Synthesis from 3D Reconstructed Rooms](https://mlsanity.com/paper/2310.15130/) (2023)
- [Differentiable Room Acoustic Rendering with Multi-View Vision Priors](https://mlsanity.com/paper/2504.21847/) (2025)
- [LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization](https://mlsanity.com/paper/2409.00819/) (2024)
- [How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes](https://mlsanity.com/paper/2508.02905/) (2025)
- [SOAF: Scene Occlusion-aware Neural Acoustic Field](https://mlsanity.com/paper/2407.02264/) (2024)
- [SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding](https://mlsanity.com/paper/2504.05576/) (2025)
- [ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling](https://mlsanity.com/paper/2404.16216/) (2024)
- [Few-shot Acoustic Synthesis with Multimodal Flow Matching](https://mlsanity.com/paper/2603.19176/) (2026)
- [Bayesian inference and neural estimation of acoustic wave propagation](https://mlsanity.com/paper/2305.17749/) (2023)
- [An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples](https://mlsanity.com/paper/2310.03349/) (2023)
- [Blind Spatial Impulse Response Generation from Separate Room- and Scene-Specific Information](https://mlsanity.com/paper/2409.14971/) (2024)
- [Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model](https://mlsanity.com/paper/2608.14097/) (2026)
- [MetaSpeech: Speech Effects Switch Along with Environment for Metaverse](https://mlsanity.com/paper/2210.13811/) (2022)
- [MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling](https://mlsanity.com/paper/2405.13860/) (2024)
- [SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field](https://mlsanity.com/paper/2406.11006/) (2024)
- [PromptReverb: Multimodal Room Impulse Response Generation Through Latent Rectified Flow Matching](https://mlsanity.com/paper/2510.22439/) (2025)

## Abstract (arXiv)

We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse reflections for a given acoustic environment. Our FAST-RIR is capable of generating RIRs for a given input reverberation time with an average error of 0.02s. We evaluate our generated RIRs in automatic speech recognition (ASR) applications using Google Speech API, Microsoft Speech API, and Kaldi tools. We show that our proposed FAST-RIR with batch size 1 is 400 times faster than a state-of-the-art diffuse acoustic simulator (DAS) on a CPU and gives similar performance to DAS in ASR experiments. Our FAST-RIR is 12 times faster than an existing GPU-based RIR generator (gpuRIR). We show that our FAST-RIR outperforms gpuRIR by 2.5% in an AMI far-field ASR benchmark.